A Study of Computer-Assisted Automatic Translation Techniques and Practices for English Vocational Terminology

This paper mainly carries out a more in-depth study on the design and planning of computer-assisted translation models based on translation memory and demonstrates the new computer-assisted translation model formed in this way. The TMX specification model, combined with the actual needs, summarizes the corresponding terminology data model, introduces and summarizes the word splitting, similarity, and alignment techniques of the computer-assisted translation model, and verifies the effectiveness of the model through practical application. The study shows that the average code length of computer-aided translation is about 0.1~0.3 lower than that of human translation in specialized fields, and its performance and efficiency are better than that of human translation. The efficiency of computer-aided translation software in extracting terminology positively correlates with an increase in frequency. Computer-assisted translation plays a significant role in the professional field and provides empirical support for subsequent related research.